Jin-Seob Kim

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it
• Signal Processing — 29 citations into it
• Speech & Audio — 19 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Optimization & Training — 6 citations into it

Whom Jin-Seob Kim trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Eunwoo Song
3. Xavier Glorot
4. Yoshua Bengio
5. Tim Salimans
6. Geoffrey E. Hinton
7. Jimmy Ba
8. Ryuichi Yamamoto
9. Alex Krizhevsky
10. Aäron van den Oord

Papers from Jin-Seob Kim's perspective

Ordered by how strongly the researchers Jin-Seob Kim trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Improved Parallel Wavegan Vocoder with Perceptually Weighted Spectrogram Loss (2021) — 18 citations
• Neural Text-to-Speech with a Modeling-by-Generation Excitation Vocoder (2020) — 6 citations